• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Modeling project schedule uncertainty due to a programmatic risk factor using bayesian networks.
【24h】

Modeling project schedule uncertainty due to a programmatic risk factor using bayesian networks.

机译：使用贝叶斯网络对由于项目风险因素导致的项目进度不确定性进行建模。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The classical method of project risk analysis, for example, the Program Evaluation and Review Technique (PERT), ignores statistical dependence among project activities, which puts limits to its effectiveness as a robust method for probabilistic schedule analysis. Also, some programmatic risk factors have been identified as significant sources of uncertainty in project performance. However, given a project that is progressing within the project life cycle, monitoring of uncertainty in the project completion times resulting from a programmatic risk factor while taking advantage of activity duration statistical dependence, has not been addressed, to the best of our knowledge. In this study, we develop two methods that are based on Bayesian Networks (BN) for evaluating and monitoring uncertainty in project completion times, when an ongoing or progressing project is impacted by a programmatic risk factor. The BN methods developed in this study model statistical dependence in project networks using parametric relationships between nodes which reduce the burden of dependence specification in the BN model. In modeling the relationships of the variables of the BN models developed in this study, concerns about computational complexities and efficient modeling of interactions of variables of a project are enabled by the capacity of the specialized Bayesian Networks software (AgenaRiskRTM) used in the analysis. Other concerns of classical PERT such as: (i) assumption of statistical independence is addressed by using conditional median as a measure of statistical dependence, and (ii) the constant PERT variance assumption is addressed using the Modified PERT Variance. Using the BN models developed in this study, we demonstrate that failure to incorporate statistical dependence grossly underestimates the total uncertainty in project completion times. The graphical dimension of our model, which benefits from the capacities of Bayesian Networks, gives more visibility about the model development and uncertainty analysis process, and which could be helpful to project analysts and managers by providing greater insight and formal mechanisms for interpreting how uncertainties in project performance measures emerge. More so, the faster learning about remaining completion time uncertainty combined with the precision of the BN approach may provide project managers more time to take corrective action to avoid schedule slippage.
机译：项目风险分析的经典方法，例如程序评估和审查技术（PERT），忽略了项目活动之间的统计依赖性，这限制了它作为概率性进度表分析的可靠方法的有效性。此外，一些计划性风险因素已被确定为项目绩效不确定性的重要来源。但是，鉴于一个在项目生命周期内进展的项目，就我们所知，在利用活动持续时间统计依赖性的同时，监视由程序性风险因素导致的项目完成时间不确定性的监视尚未得到解决。在这项研究中，我们开发了两种基于贝叶斯网络（BN）的方法，用于评估和监视项目进行时间的不确定性（当正在进行或进行中的项目受到程序性风险因素的影响时）。本研究中开发的BN方法使用节点之间的参数关系对项目网络中的统计依赖关系进行建模，从而减少了BN模型中依赖关系说明的负担。在对本研究中开发的BN模型的变量之间的关系进行建模时，可以通过分析中使用的专用贝叶斯网络软件（AgenaRiskRTM）的功能来实现对计算复杂性和项目变量交互的有效建模的关注。经典PERT的其他问题，例如：（i）使用条件中位数作为统计依赖性的量度来解决统计独立性的假设，以及（ii）使用修正的PERT方差来解决恒定PERT方差的假设。使用在这项研究中开发的BN模型，我们证明了未能纳入统计依赖性严重低估了项目完成时间的总不确定性。我们的模型的图形化维度得益于贝叶斯网络的能力，可提供有关模型开发和不确定性分析过程的更多可见性，并且可以通过提供更多的见解和形式化的机制来解释不确定性如何在模型中发挥作用，从而对项目分析人员和管理人员有所帮助。项目绩效指标应运而生。更重要的是，对剩余完工时间不确定性的更快了解与BN方法的精度相结合，可以为项目经理提供更多时间采取纠正措施，以避免进度拖延。

著录项

  • 作者

    Nduka, Ifechukwu C.;

  • 作者单位

    The George Washington University.;

  • 授予单位 The George Washington University.;
  • 学科 Operations research.;Management.;Industrial engineering.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 151 p.
  • 总页数 151
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号